Digital Commons Network^™

Democratizing Community Discourse Analysis In Computational Social Science, Md Towhidul Absar Chowdhury

Theses

Community resource inequities and undetected infrastructure vulnerabilities cost municipalities billions annually, with disproportionate impacts on marginalized communities. Current computational approaches to community needs assessment suffer from two critical limitations: they rely on aggregate-level analysis that obscures granular community expressions, and they implement sophisticated computational tools that remain inaccessible to stakeholders without technical expertise. This creates what we term the Community-Computational Gap—a persistent divide where domain experts with vital contextual knowledge cannot access the analytical tools they need, while computational experts develop models without sufficient community context. This dissertation addresses these challenges through a novel methodological framework for fine-grained utterance-level classification …

Physics-Based Machine Learning Framework For Predicting Structure-Property Relationships In Ded-Fabricated Low-Alloy Steels †, Atiqur Rahman, Md Hazrat Ali, Asad Waqar Malik, Muhammad Arif Mahmood, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The Directed Energy Deposition (DED) process has demonstrated high efficiency in manufacturing steel parts with complex geometries and superior capabilities. Understanding the complex interplays of alloy compositions, cooling rates, grain sizes, thermal histories, and mechanical properties remains a significant challenge during DED processing. Interpretable and data-driven modeling has proven effective in tackling this challenge, as machine learning (ML) algorithms continue to advance in capturing complex property structural relationships. However, accurately predicting the prime mechanical properties, including ultimate tensile strength (UTS), yield strength (YS), and hardness value (HV), remains a challenging task due to the complex and non-linear relationships among process …

Evaluating Dental Ai Research Papers: Key Considerations For Editors And Reviewers, Sergio E. Uribe, Manal H. Hamdan, Nicola Alberto Valente, Satoshi Yamaguchi, Fahad Umer, Antonin Tichy, Ruben Pauwels, Falk Schwendicke

School of Dentistry Faculty Research and Publications

Objective

Artificial intelligence (AI) is increasingly used in dental research for diagnosis, treatment planning, and disease prediction. However, many dental AI studies lack methodological rigor, transparency, or reproducibility, and no dedicated peer-review guidance exists for this field.

Methods

Editors and reviewers from the ITU/WHO/WIPO AI for Health – Dentistrygroup participated in a structured survey and group discussions to identify key elements for reviewing AI dental research. A draft of the recommendations was circulated for feedback and consensus.

Results

The consensus from editors and reviewers identified four key indicators of high-quality AI dental research: (1) relevance to a real clinical …

Sugar: A Sequence Unfolding Based Transformer Model For Group Activity Recognition, Yash U. Gondkar

Graduate Masters Theses

Large Language Models have improved significantly in the past couple of years due to the adoption of transformers. However, transformers still find it challenging to process videos due to limited context size caused by their quadratic computing cost. Therefore, we studied a booming field in machine learning which powers applications like social scene analysis and video surveillance systems called Group Activity Recognition (GAR). We found that recent models were able to achieve more than 90% accuracy on popular datasets like the Volleyball dataset, however, it turned out that even they relied on transformers.

Therefore, in this work, we developed a …

The Research University, Industry And Artificial Intelligence, Hamlet Isakhanli

Khazar Journal of Humanities and Social Sciences

Scientific research in cooperation between science and industry occurs mainly in research universities. This article begins with a brief history of research universities, a discussion of their nature and form. Their evolution and development in Europe, the USA and China are recalled; the special importance of Graduate Schools is underlined. Cooperation between the worlds of university and industry is studied using the example in the USA of Silicon Valley, a world leader in its field and initiated by Stanford University. Presented alongside the positive American experience, is the rather negative experience of the ``Russian syndrome''. The development of semiconductor, transistor …

A Comparative Of Detecting Physical And Cyber Attacks On Drones Using Machine Learning And Deep Learning Techniques, Khattab M Ali Alheeti, Sara Abbas Rafa, Maha Mahmood, Aythem Khairi Kareem, Mohammad Aljanabi, Ahmed Adil Nafea

Baghdad Science Journal

Multitudes of Unmanned Aerial Vehicles (UAVs) are generally embraced in military and civilian applications. Yet, this physical-cyber method is intimidated by cyber-attacks. Recently, Machine Learning (ML) based attacks detection approaches have been effectively embraced to detect cyber-attacks. This paper presented the Intrusion Detection Security (IDS) approach. The proposed approach investigates UAVs' cyber and physical attributes under normal process and attack circumstances. Two types of cyber-attacks have been classified: Denial-of-service (DoS) and replay. This study developed IDS approaches established on ML and Deep Learning (DL) prototypes, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), and One Dimensional Convolutional …

Mathematics And Mental Health: An Interdisciplinary Analysis Of Ai In Counselor Education, Jennifer M. Hightower, Catrina A. May

Journal of Counselor Preparation and Supervision

Although use of Artificial Intelligence (AI) in mental health care has become increasingly common, many professional counselors remain under informed about foundational components of AI. Fundamental issues associated with AI, including model bias and the Black Box Problem, must be considered as these tools become integrated into the counseling profession. This manuscript provides an interdisciplinary, theoretical analysis of AI use in counseling and counselor education grounded in foundational knowledge of AI and the American Counseling Association’s recommendations for the ethical integration of AI (Butler et al., 2023). The paper summarizes these recommendations, establishes accessible definitions of AI terms, explains the …

Improving Arabic Coffee Production Through Disease Detection Based Ai Frameworks, A.S. Balobaid, R. Alarabi, S.M. Abd Elazim

Yanbu Journal of Engineering and Science

Coffee is the second most traded commodity globally after oil and represents a major source of income in many countries. Kingdom of Saudi Arabia has a great interest in growing and expanding of coffee in line with the Saudi Green Initiative as implementation of Vision 2030 AD. This expansion is accompanied by a greater spread of insect pests and the emergence of diseases affecting coffee. Recently, machine learning technologies have been beneficial in the agricultural era in detection and classification of fruit and tree diseases. The study initially focuses on kind of disease that appears in the leafy area of …

Retracted: Iot Flow Parameters Classification Based On Machine Learning Techniques, El-Sayed M. El-Kenawy, Marwa M. Eid, Ban Salman Shukur, Amel Ali Alhussan, Doaa Sami Khafaga

Iraqi Journal for Computer Science and Mathematics

In recent years, there has been a highly remarkable convergence of artificial intelligence (AI) and the Internet of Things (IoT), which has made rapid progress in smart city initiatives by developing smart devices for such cities. Since these devices are increasingly diversified, they require a resilient communication network to demonstrate high performance in managing consistent traffic flows. A machine learning model intended for identifying network parameters from diverse devices, in addition to proposing modifications meant for network performance enhancement, is developed in this study. In relation to packet data as a network traffic parameter, employing gateway devices can facilitate its …

Modeling The Importance Of Life Exposure Factors On Memory Performance In Diverse Older Adults: A Machine Learning Approach, Evan Fletcher, Marianne Chanti-Ketterl, Emily Hokett, Yi Lor, Umesh Venkatesan, Ruijia Chen, Omonigho M. Bubu, Rachel Whitmer, Paola Gilsanz, Zvinka Z. Zlatar

Moss-Magee Rehabilitation Papers

INTRODUCTION: Many health life exposure factors (LEFs) influence cognitive decline and dementia incidence, but their relative importance to episodic memory (an early indicator of cognitive decline) among diverse older adults is unclear. We used machine learning to rank LEFs for memory performance in a large and diverse US cohort.

METHODS: Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) and Study of Healthy Aging in African Americans (STAR), participants underwent neuropsychological testing and answered questionnaires about multiple LEFs. XGBoost and Shapley Additive exPlanation values ranked the importance of factors influencing cross-sectional episodic memory in the full sample and by sex and …

Machine Learning-Enhanced Optimization For The Real-Time, Secure, And Efficient Operation Of The Energy-Water-Hydrogen Nexus, Mostafa Goodarzi

Graduate Thesis and Dissertation post-2024

Given the escalating urgency of climate change, our research addresses the imperative need to curb carbon emissions, particularly from the electricity sector, which accounts for a quarter of total emissions in the U.S. We propose integrated systems, such as the energy-water nexus (EWN) and an innovative concept known as the energy-water-hydrogen (EWH) nexus, which integrates renewable energy sources (RESs) with green hydrogen production through water electrolysis. These concepts align seamlessly with the nation’s commitment to global climate agreements, including the Paris Agreement. Furthermore, green hydrogen—a clean and efficient energy source produced via water electrolysis powered by renewable energy—emerges as a …

Research On Autonomous Deviation Correction Of Tunnel Boring Machines And Parameters Based On Machine Learning, Zhang Jun, Li Maopeng

Journal of China & Foreign Highway

In order to solve the problem of realizing the autonomous deviation correction of tunnel boring machines (TBMs ), a TBM deviation correction control method that integrated the random forest (RF) algorithm with the genetic algorithm (GA) was proposed based on actual engineering data.The method combined a prediction model with an optimization model,using target deviation values as input to invert and output the required TBM deviation correction parameter values,thereby further improving the automation level of TBM deviation correction.By comparing it with the actual data,the feasibility of the model was verified.The results show that the RF algorithm-based prediction model achieves an R2 …

Spectroscopy Pre-Trained Transformer (Specpt): A Universal Spectroscopic Analysis And Redshift Measurement Framework, Rohan Pattnaik

Theses

Spectroscopic surveys are essential for measuring galaxy redshifts and probing the physical processes driving galaxy evolution. As datasets grow in scale and complexity, traditional analysis methods face increasing limitations, motivating the development of scalable, data-driven alternatives. This thesis presents SpecPT, a transformer-based deep learning framework for general-purpose spectroscopic analysis, with a focus on redshift estimation. The model is first trained on DESI Early Data Release spectra from the Bright Galaxy and Emission Line Galaxy samples, jointly performing spectral reconstruction and redshift regression while learning a latent representation that captures the intrinsic properties of galaxies. SpecPT is then extended to a …

Ai-Driven Biomedical Perspectives On Mental Fatigue In The Post-Covid-19 Era: Trends, Research Gaps, And Future Directions, Saba Parveen, Md Belal Bin Heyat, Umair Tariq, Faijan Akhtar, Hafiz Muhammad Zeeshan, Seth Christopher Yaw Appiah, Shang-Ming Zhou, Huang Lei

School of Nursing and Midwifery

Mental fatigue is a complex condition arising from various neurological processes and influenced by external factors such as stress and cognitive demands. This comprehensive review elucidates the primary neurological mechanisms underlying mental fatigue, particularly emphasizing how it was elevated or otherwise affected during the COVID-19 pandemic. We explore the intricate relationship between prolonged cognitive tasks, chronic stress, and the development of mental fatigue, emphasizing the impacts that mental fatigue has on mental health across diverse populations. Utilizing advanced artificial intelligence techniques, including machine learning and deep learning, this study identifies and quantifies the patterns of mental fatigue. The innovative approach …

Assessing Greenhouse Gas Emissions From Michigan’S Drowned River Mouths Using In Situ And Remote Sensing Methods, Jillian A. Greene

Masters Theses

Freshwater estuaries are natural contributors to the carbon cycle including production and emission of methane (CH4) and carbon dioxide (CO2), potent greenhouse gases (GHGs); however, estimates of their contribution to regional and global GHG emissions is largely unconstrained. Few studies have examined the quantification and drivers of lake CH4 and CO2 production in the Great Lakes region and how it may differ in response to anthropogenic development. In this study, CH4 and CO2 emissions were measured from three drowned river mouth estuaries (DRMs) along the eastern shore of Lake Michigan in 2024. The DRMs exist along a latitudinal gradient ranging …

Mri-Based Radiomics For Outcome Stratification In Pediatric Osteosarcoma, Esther Ngan, Dolores Mullikin, Ashok J Theruvath, Ananth V Annapragada, Ketan B Ghaghada, Andras A Heczey, Zbigniew A Starosolski

Faculty, Staff and Students Publications

Background/Objectives: Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents; the survival rate is as low as 24%. Accurate prediction of clinical outcomes remains a challenge due to tumor heterogeneity and the complexity of pediatric cases. This study aims to improve predictions of progressive disease, therapy response, relapse, and survival in pediatric OS using MRI-based radiomics and machine learning methods. \

Methods: Pre-treatment contrast-enhanced coronal T1-weighted MR scans were collected from 63 pediatric OS patients, with an additional nine external cases used for validation. Three strategies were considered for target region segmentation (whole-tumor, tumor …

Development Of A Machine Learning Framework For An Irrigation Decision Support System, Eric Wilkening, Derek M. Heeren, Yeyin Shi, Laila A. Puntel, Guillermo R. Balboa, Abia Katimbo, Kuan Zhang, Precious Nneka Amori, Bruno Lena

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Currently used irrigation scheduling techniques often require significant human intervention and are time consuming, particularly for variable rate irrigation. This research aimed to develop a conceptual framework that incorporates disparate data sources leveraging both mechanistic and machine learning (ML) approaches. The overarching objective is to utilize the growing availability of data and technology to manage irrigation more precisely, which will minimize negative impacts of irrigation on our water resources. An initial irrigation machine learning model was proposed and tested in this study as a key component of an edge-cloud computing and federated learning decision-making framework. The specific objectives were (1) …

Modeling For Functional Traits In Bluebunch Wheatgrass Using Unmanned Aerial Vehicle (Uav) High-Throughput Phenotyping, Brittany L. Johnson

All Graduate Theses and Dissertations, Fall 2023 to Present

Changing climates and shifts in community composition in the Intermountain West threaten sagebrush-steppe ecosystems, impacting both plants and animals. Restoring native grasses like bluebunch wheatgrass (Pseudoroegneria spicata; BBWG) can help ecosystems resist invasive species and recover from stress and disturbances. As a native cool-season grass, BBWG is widely used in rangeland restoration for the western U.S. Breeding programs for restoration plant materials, such as seeds for ecological projects, often require years or decades of field testing while facing staffing constraints. High- throughput phenotyping (HTP), which rapidly measures observable plant traits, offers a faster solution using drones to capture time-series …

Discovery Of High-Performance Cathode Materials For Protonic Ceramic Fuel Cells, Liang Han

All Dissertations

Environmental pollution and rapid energy consumption have become common problems in global development and will continue to grow with the world population. PCFCs use proton-conducting ceramics as electrolytes, with low activation energy and high ionic conductivity at intermediate temperatures, enabling them to operate at intermediate-temperature conditions, which can effectively solve the problems of poor stability and high cost of exotic materials of traditional solid oxide fuel cells. However, as the operating temperature decreases, the electrocatalytic activity of the cathode decreases significantly, seriously affecting PCFC’s performance. Therefore, developing high-performance cathode material suitable for working under intermediate-temperature conditions has become the key …

Advancing The Utility Of Unmanned Aerial Systems (Uas)-Based Imaging Techniques In Broadacre Agriculture: A Multimodal Case Study On Table Beets, Mohammad Shahriar Saif

Theses

Efficient and sustainable food production and management are growing concerns in the context of an ever-increasing global population. It is in this context that the integration of remote sensing with advanced imaging technologies presents transformative opportunities for data-driven decision-making in agriculture. This study therefore explores the use of unmanned aerial systems (UAS) equipped with multispectral, hyperspectral, and LiDAR sensors for the non-destructive monitoring of table beet (Beta vulgaris) crop traits, with a specific focus on root yield estimation and foliar disease assessment. Table beet, a subterranean crop of increasing commercial and nutritional importance, poses unique challenges for above-canopy sensing due …

A Focus On Student Education: Determining Student Attitudes Towards Transparent Autograding And Developing Artificially Intelligent Tools To Help Students Succeed, Andra Rice

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis is composed of two parts both relating to helping students succeed. First, the focus is on determining how we can help students feel more comfortable using an AI tool that can provide them immediate feedback. Second, machine learning algorithms are explored in relation to tracking student tasks to encourage healthy study habits.

The development of effective autograders is key for scaling assessment and feedback. While AI based autograding systems for open-ended response questions have been found to be beneficial for providing immediate feedback, autograders are not always liked, understood, or trusted by students. Our research tested the effect …

Integrating Uas And Satellite Data To Improve Classification Of Plant Functional Types In A Forest Landscape, Sky Mae Gennette

Boise State University Theses and Dissertations

Remotely sensed land-use/land-cover (LULC) data products are an important tool for understanding landscape processes at all scales. The types of inference researchers can derive from remote sensing data depend on the quality and characterization of these LULC products. Currently, gaps in the detail of widely available vegetation cover products limit inference about processes at the level of plant functional type (PFT). Advanced remote sensing tools, machine learning, and data fusion can be employed to contribute novel methods to produce detailed, accurate vegetation cover products. Here, we combine high resolution Unoccupied Aerial Systems (UAS) multispectral, structural and textural data with large-scale …

Birth Outcomes In The Hispanic Population In The United States: Trends, Variation, And Determinants (2011–2021), Yanchao Yang

Business Analytics Faculty Publications

Background: Cesarean section (C-section) is the most common surgical procedure in the United States, yet its use varies widely across regions and institutions. Although clinical risk factors are central to delivery decisions, geographic context, health system capacity, and local practice patterns may also influence C-section use. Understanding both the determinants and predictability of C-section delivery is important for improving obstetric quality and equity.

Objective: This study aims to document geographic variation in C-section use across the United States, identify maternal and county-level factors associated with C-section delivery, and evaluate the predictive performance of machine learning models across clinically defined risk …

Comparative Analysis Of Sequential And Non-Sequential Modeling Techniques For Ddos Attack Detection With Explainable Ai, Vincent Agbenyeavu

Theses and Dissertations

Cybersecurity is known today as one of the greatest challenges of the modern era. Among the various types of cyber-attacks that threaten our security, the Distributed Denial of Service (DDoS) attack is among some of the most common, effective, and well-recognized attack strategies. Since this form of attack is meant to disrupt the availability factor covertly, it can be detrimental to the targeted machines and difficult to discover. Because of that, there have been several approaches, as well as solutions that have been devised to detect it as accurately and efficiently as possible. In this study, four sequential data modeling …

Low-Cost Monitoring And Fingerprinting Of High-Powered Electric Systems, Kwabena Buamono Aboagye-Otchere

Theses and Dissertations

Electric motors are vital to industry, transport, and energy, yet their maintenance challenges persist. While traditional reactive maintenance leads to costly downtime and safety risks, predictive maintenance, especially through IoT and machine learning offers early fault detection and operational efficiency. However, this shift introduces security concerns due to unintended magnetic emissions from motors. These emissions, though useful for non-intrusive monitoring, can be exploited to eavesdrop on sensitive industrial processes. This dissertation explores the dual nature of magnetic emissions: their value in motor diagnostics and their potential as a security vulnerability. It demonstrates how emissions can identify motors, monitor health, and …

Computational Learning Across Biological And Industrial Systems: Bayesian And Segmentation Models For Epigenomic And Manufacturing Data, Prosenjit Roy

All Theses

In this thesis, we made a complete dual-domain investigation using a machine learning approach into two different scientific areas which were epigenetic analysis of seal species and pore identification in the work of additive manufacturing. The study showcased the flexibility and strength of modern computational tools in addressing complicated issues in various biological and industrial systems. We further investigated the epigenetics by using the Bayesian Neural Network and other machine learning methods to conduct a study on the DNA methylation pattern within three pinniped species (the northern elephant seal, Hawaiian monk seal, and Weddell seal) with perfect precision on species …

Non-Blackbox Robust Design Of Machine Learning In Networks, Venkat Sai Suman Lamba Karanam

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Networked systems have become increasingly complex, with newer communication technologies and standards being added every day. Machine Learning (ML) and Artificial Intelligence (AI) paradigms have been adopted in networks to not only solve many fundamental problems, but also to allow seamless integration of components comprising them. The saying “let’s not reinvent the wheel” in ML/AI adoption implies that model architecture design be left for pure ML/AI researchers, while network researchers focus on input preprocessing (e.g. formatting the packet data to be fed to a model), hyperparameter fine-tuning and a trial-and-error approach to find the “best” result. …

What Values Best Distinguish The World's Cultures? The Machine Learning-Based Cultural Values Inventory, Abhishek Sheetal, Shilpa Madan, Rui Ling Lee, Krishna Savani

Research Collection Lee Kong Chian School Of Business

Scientists studying culture typically focus on a small number of theoretical constructs, such as individualism–collectivism, when seeking to explain cultural differences in psychological tendencies and behaviors. However, existing theories of culture could have missed out on important constructs that are useful for explaining cross-cultural differences. We used an abductive approach combining prediction and explanation to uncover important cultural values. In the prediction phase, based on 594 attitudes, values, and beliefs included in the World Values Survey, a neural network could classify respondents’ nationalities with 90% accuracy in out-of-sample data. In the explanation phase, a feature importance analysis identified the values …

Use Of A Preliminary Artificial Intelligence-Based Laryngeal Cancer Screening Framework For Low-Resource Settings: Development And Validation Study, Shao Wei Sean Lam, Min Hun Lee, Michael Dorosan, Samuel Altonji, Hiang Khoon Tan, Walter T. Lee

Research Collection School Of Computing and Information Systems

Background: Early-stage diagnosis of laryngeal cancer significantly improves patient survival and quality of life. However, the scarcity of specialists in low-resource settings hinders the timely review of flexible nasopharyngoscopy (FNS) videos, which are essential for accurate triage of at-risk patients.Objective: We introduce a preliminary AI-based screening framework to address this challenge for the triaging of at-risk patients in low-resource settings. This formative research addresses multiple challenges common in high-dimensional FNS videos: (1) selecting clear, informative images; (2) deriving regions within frames that show an anatomical landmark of interest; and (3) classifying patients into referral grades based on the FNS video …

L2m2: A Hierarchical Framework Integrating Large Language Model And Multi‑Agent Reinforcement Learning, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Lin Li, Xin Zhao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning (MARL) has demonstrated remarkable success in collaborative tasks, yet faces significant challenges in scaling to complex scenarios requiring sustained planning and coordination across long horizons. While hierarchical approaches help decompose these tasks, they typically rely on hand-crafted subtasks and domain-specific knowledge, limiting their generalizability. We present L2M2, a novel hierarchical framework that leverages large language models (LLMs) for high-level strategic planning and MARL for low-level execution. L2M2 enables zero-shot planning that supports both end-to-end training and direct integration with pre-trained MARL models. Experiments in the VMAS environment demonstrate that L2M2's LLM-guided MARL achieves superior performance while requiring …